Survey-led brand tracking is a constant trade-off between accuracy and cost. The larger the sample size, the more reliable the insights, but invariably the larger the budget required. Nepa’s AI Trend Boost is your solution.
This revolutionary machine-learning based approach takes historical tracking data, analyses context and covariance between key variables, and then creates a model with the same accuracy as a sample with five times the data. It also reduces sampling variances by up to 75%, bringing you closer than ever to your consumer.
– Offers a 5 times increase in the depth and granularity of your data
– Enhances low incidence target groups to create meaningful business insights
– Produces insights with the smoothness of a moving average, combined with the responsiveness of near-time data
How does AI Trend Boost Work?
If you have a small sample, unboosted raw data in your brand tracker can have notable weekly shifts. How do you make confident business decisions when you can’t tell what’s a concrete trend, and what is simply noise?
AI Trend Boost uses a proprietary algorithm to analyse several key data variables and past-time series data to generate an accurate model for your brand’s KPIs. It learns and improves over time – the more data it is given, the more refined its predictions become, making it an invaluable tool to optimise your brand’s performance and stay ahead of trends.
In almost all simulated cases, the output of a tracker with AI Trend Boost applied is far superior to a simple moving average in terms of real-life population trend accuracy, including in those with decreased sample sizes. Error metrics are reduced by up to 75%.
Plus, AI Trend Boost is easy to integrate into our core Brand Trackers, letting you effectively enhance your insights with a single click.
Industry recognition for AI Trend Boost
AI Trend Boost’s Brand Noise Reduction methodology has been recognised by industry experts as cutting-edge technology, and it is trusted by the biggest global brands including IKEA.
“Applying Nepa’s Brand Noise Reduction algorithm to Marketing Mix Modelling really gives us peace of mind that we’re making decisions based on the best data possible.”
Brinda Matthew, Head of Brand Marketing Performance, IKEA Canada